Public Sentiment Analysis on Instagram Regarding the Film "Pengepungan di Bukit Duri" Using Naïve Bayes Approach - Azzizah, Putri Salfa Dhiyaa; Yel, Mesra Betty (Journal Innovations Computer Science, 2025)
This study investigates public sentiment toward Joko Anwar’s 2025 film Pengepungan di Bukit Duri using computational text analysis on 583 Instagram comments. The research applies the Naïve Bayes algorithm combined with TF-IDF weighting to classify opinions into positive and negative sentiments. Data were collected through web scraping of public Instagram posts related to the film and processed through several stages including data cleaning, manual labeling, text preprocessing, and probabilistic classification. The results reveal that 72.9% of the comments express positive sentiment, while 27.1% are negative, indicating strong audience appreciation for the film’s narrative quality and social themes. The model achieved an accuracy of 83.67%, with a precision of 87.13%, recall of 91.04%, and F1-score of 89.04% for positive sentiment. These findings confirm that the Naïve Bayes approach is effective for analyzing short, informal Indonesian-language texts on social media. Practically, the results provide valuable insights for filmmakers and cultural analysts in understanding audience perceptions, managing digital reputation, and designing sentiment-based marketing strategies. Future research is recommended to employ hybrid models and multi-platform datasets to enhance sentiment detection, particularly for nuanced or negative expressions.